Mechanistic models for West Nile virus transmission: a systematic review of features, aims and parametrization.

Mathematical models within the Ross-Macdonald framework increasingly play a role in our understanding of vector-borne disease dynamics and as tools for assessing scenarios to respond to emerging threats. These threats are typically characterized by a high degree of heterogeneity, introducing a range of possible complexities in models and challenges to maintain the link with empirical evidence. We systematically identified and analysed a total of 77 published papers presenting compartmental West Nile virus (WNV) models that use parameter values derived from empirical studies. Using a set of 15 criteria, we measured the dissimilarity compared with the Ross-Macdonald framework. We also retrieved the purpose and type of models and traced the empirical sources of their parameters. Our review highlights the increasing refinements in WNV models. Models for prediction included the highest number of refinements. We found uneven distributions of refinements and of evidence for parameter values. We identified several challenges in parametrizing such increasingly complex models. For parameters common to most models, we also synthesize the empirical evidence for their values and ranges. The study highlights the potential to improve the quality of WNV models and their applicability for policy by establishing closer collaboration between mathematical modelling and empirical work.

Indirect interaction between an endemic and an invading pathogen: A case study of Plasmodium and Usutu virus dynamics in a shared bird host population.

Infectious disease agents can influence each other's dynamics in shared host populations. We consider such influence for two mosquito-borne infections where one pathogen is endemic at the time that a second pathogen invades. We regard a setting where the vector has a bias towards biting host individuals infected with the endemic pathogen and where there is a cost to co-infected hosts. As a motivating case study, we regard Plasmodium spp., that cause avian malaria, as the endemic pathogen, and Usutu virus (USUV) as the invading pathogen. Hosts with malaria attract more mosquitoes compared to susceptible hosts, a phenomenon named vector bias. The possible trade-off between the vector-bias effect and the co-infection mortality is studied using a compartmental epidemic model. We focus first on the basic reproduction number R0 for Usutu virus invading into a malaria-endemic population, and then explore the long-term dynamics of both pathogens once Usutu virus has become established. We find that the vector bias facilitates the introduction of malaria into a susceptible population, as well as the introduction of Usutu in a malaria-endemic population. In the long term, however, both a vector bias and co-infection mortality lead to a decrease in the number of individuals infected with either pathogen, suggesting that avian malaria is unlikely to be a promoter of Usutu invasion. This proposed approach is general and allows for new insights into other negative associations between endemic and invading vector-borne pathogens.

Overlapping timescales obscure early warning signals of the second COVID-19 wave.

Early warning indicators based on critical slowing down have been suggested as a model-independent and low-cost tool to anticipate the (re)emergence of infectious diseases. We studied whether such indicators could reliably have anticipated the second COVID-19 wave in European countries. Contrary to theoretical predictions, we found that characteristic early warning indicators generally decreased rather than increased prior to the second wave. A model explains this unexpected finding as a result of transient dynamics and the multiple timescales of relaxation during a non-stationary epidemic. Particularly, if an epidemic that seems initially contained after a first wave does not fully settle to its new quasi-equilibrium prior to changing circumstances or conditions that force a second wave, then indicators will show a decreasing rather than an increasing trend as a result of the persistent transient trajectory of the first wave. Our simulations show that this lack of timescale separation was to be expected during the second European epidemic wave of COVID-19. Overall, our results emphasize that the theory of critical slowing down applies only when the external forcing of the system across a critical point is slow relative to the internal system dynamics.

The effects of cow introductions on milk production and behaviour of the herd measured with sensors.

This research paper addresses the hypothesis that cow introductions in dairy herds affect milk production and behaviour of animals already in the herd. In dairy farms, cows are commonly regrouped or moved. Negative effects of regroupings on the introduced animals are reported in other studies. However, little is known about the effects on lactating cows in the herd. In this research a herd of 53 lactating dairy cows was divided into two groups in a cross-over design study. 25 cows were selected as focal cows for which continuous sensor data were collected. The treatment period consisted of replacing non-focal cows three times a week. Many potentially influencing factors were taken into account in the analysis. Replacement of cows in the treatment period indeed affected the focal animals. During the treatment period these cows showed increased walking and reduced rumination activity and produced less milk compared to the control period. Milk production per milking decreased in the treatment period up to 0.4 kg per milking on certain weekdays. Lying and standing behaviour were similar between the control and the treatment period. The current study suggests that cow introductions affect welfare and milk production of the cows already in the herd.

Key questions for modelling COVID-19 exit strategies.

Combinations of intense non-pharmaceutical interventions (lockdowns) were introduced worldwide to reduce SARS-CoV-2 transmission. Many governments have begun to implement exit strategies that relax restrictions while attempting to control the risk of a surge in cases. Mathematical modelling has played a central role in guiding interventions, but the challenge of designing optimal exit strategies in the face of ongoing transmission is unprecedented. Here, we report discussions from the Isaac Newton Institute 'Models for an exit strategy' workshop (11-15 May 2020). A diverse community of modellers who are providing evidence to governments worldwide were asked to identify the main questions that, if answered, would allow for more accurate predictions of the effects of different exit strategies. Based on these questions, we propose a roadmap to facilitate the development of reliable models to guide exit strategies. This roadmap requires a global collaborative effort from the scientific community and policymakers, and has three parts: (i) improve estimation of key epidemiological parameters; (ii) understand sources of heterogeneity in populations; and (iii) focus on requirements for data collection, particularly in low-to-middle-income countries. This will provide important information for planning exit strategies that balance socio-economic benefits with public health.

Exploring vector-borne infection ecology in multi-host communities: A case study of West Nile virus.

In this study, we develop a model to investigate how ecological factors might affect the dynamics of a vector-borne pathogen in a population composed by different hosts which interact with each other. Specifically, we consider the case when different host species compete with each other, as they share the same habitat, and the vector might have different feeding preference, which can also be time dependent. As a prototypical example, we apply our model to study the invasion and spread, during a typical season, of West Nile virus in an ecosystem composed of two competent avian host species and possibly of dead-end host species. We found that competition and vector feeding preferences can profoundly influence pathogen invasion, influencing its probability to start an epidemic, and influencing transmission rates. Finally, when considering time-dependent feeding preferences, as observed in the field, we noted that the virus circulation could be amplified and that the timing of epidemic peaks could be changed. Our work highlights that ecological interactions between hosts can have a profound influence on the dynamics of the pathogen and that, when modeling vector-borne infections, vector feeding behavior should, for this reason, be carefully evaluated.

Infectious disease agents mediate interaction in food webs and ecosystems.

Infectious agents are part of food webs and ecosystems via the relationship with their host species that, in turn, interact with both hosts and non-hosts. Through these interactions, infectious agents influence food webs in terms of structure, functioning and stability. The present literature shows a broad range of impacts of infectious agents on food webs, and by cataloguing that range, we worked towards defining the various mechanisms and their specific effects. To explore the impact, a direct approach is to study changes in food-web properties with infectious agents as separate species in the web, acting as additional nodes, with links to their host species. An indirect approach concentrates not on adding new nodes and links, but on the ways that infectious agents affect the existing links across host and non-host nodes, by influencing the 'quality' of consumer-resource interaction as it depends on the epidemiological state host involved. Both approaches are natural from an ecological point of view, but the indirect approach may connect more straightforwardly to commonly used tools in infectious disease dynamics.

Identifying transmission cycles at the human-animal interface: the role of animal reservoirs in maintaining gambiense human african trypanosomiasis.

Many infections can be transmitted between animals and humans. The epidemiological roles of different species can vary from important reservoirs to dead-end hosts. Here, we present a method to identify transmission cycles in different combinations of species from field data. We used this method to synthesise epidemiological and ecological data from Bipindi, Cameroon, a historical focus of gambiense Human African Trypanosomiasis (HAT, sleeping sickness), a disease that has often been considered to be maintained mainly by humans. We estimated the basic reproduction number [Formula: see text] of gambiense HAT in Bipindi and evaluated the potential for transmission in the absence of human cases. We found that under the assumption of random mixing between vectors and hosts, gambiense HAT could not be maintained in this focus without the contribution of animals. This result remains robust under extensive sensitivity analysis. When using the distributions of species among habitats to estimate the amount of mixing between those species, we found indications for an independent transmission cycle in wild animals. Stochastic simulation of the system confirmed that unless vectors moved between species very rarely, reintroduction would usually occur shortly after elimination of the infection from human populations. This suggests that elimination strategies may have to be reconsidered as targeting human cases alone would be insufficient for control, and reintroduction from animal reservoirs would remain a threat. Our approach is broadly applicable and could reveal animal reservoirs critical to the control of other infectious diseases.

The changing incidence of dengue haemorrhagic fever in Indonesia: a 45-year registry-based analysis.

BACKGROUND: Increases in human population size, dengue vector-density and human mobility cause rapid spread of dengue virus in Indonesia. We investigated the changes in dengue haemorrhagic fever (DHF) incidence in Indonesia over a 45-year period and determined age-specific trends in annual DHF incidence. METHODS: Using an on-going nationwide dengue surveillance program starting in 1968, we evaluated all DHF cases and related deaths longitudinally up to 2013. Population demographics were used to calculate annual incidence and case fatality ratios (CFRs). Age-specific data on DHF available from 1993 onwards were used to assess trends in DHF age-distribution. Time-dependency of DHF incidence and CFRs was assessed using the Cochrane-Armitage trend test. RESULTS: The annual DHF incidence increased from 0.05/100,000 in 1968 to ~ 35-40/100,000 in 2013, with superimposed epidemics demonstrating a similar increasing trend with the highest epidemic occurring in 2010 (85.70/100,000; p < 0.01). The CFR declined from 41% in 1968 to 0.73% in 2013 (p < 0.01). Mean age of DHF cases increased during the observation period. Highest incidence of DHF was observed among children aged 5 to 14 years up to 1998, but declined thereafter (p < 0.01). In those aged 15 years or over, DHF incidence increased (p < 0.01) and surpassed that of 5 to 14 year olds from 1999 onwards. CONCLUSIONS: Incidence of DHF over the past 45 years in Indonesia increased rapidly with peak incidence shifting from young children to older age groups. The shifting age pattern should have consequences for targeted surveillance and prevention.

Dynamics of the plague-wildlife-human system in Central Asia are controlled by two epidemiological thresholds.

Plague (caused by the bacterium Yersinia pestis) is a zoonotic reemerging infectious disease with reservoirs in rodent populations worldwide. Using one-half of a century of unique data (1949-1995) from Kazakhstan on plague dynamics, including data on the main rodent host reservoir (great gerbil), main vector (flea), human cases, and external (climate) conditions, we analyze the full ecoepidemiological (bubonic) plague system. We show that two epidemiological threshold quantities play key roles: one threshold relating to the dynamics in the host reservoir, and the second threshold relating to the spillover of the plague bacteria into the human population.

Warming effects on the life cycles of two parasitic copepods with different invasion histories.

Climate change may exacerbate the impact of invasive parasites from warmer climates through pre-existing temperature adaptations. We investigated temperature impacts on two closely related marine parasitic copepod species that share the blue mussel (Mytilus edulis) as host: Mytilicola orientalis has invaded the system from a warmer climate <20 years ago, whereas its established congener Mytilicola intestinalis has had >90 years to adapt. In laboratory experiments with temperatures 10-26°C, covering current and future temperatures as well as heat waves, the development of both life cycle stages of both species accelerated with increasing temperature. In the parasitic stages, the growth of the established invader increased evenly from 10°C to 22°C, whereas the recent invader barely grew at all at 10°C and grew faster already at 18°C. In contrast, temperature had little effect on the transition success between life cycle stages. However, the highest temperature (26°C) limited the egg development success of the established invader and the host entry success of both species, whereas the infection success of the established invader increased at 18°C and 22°C. In general, our experiments indicate that the main effect of temperature on both species is through development speed and not life cycle stage transition success. Based on regional long-term temperature data and predictions, the numbers of completed life cycles per year will increase for both parasites. The established invader seems better adapted for low current temperatures (around 10°C), whereas the more recent invader barely develops at these temperatures but can cope in high temperatures (around 26°C). Hence, pre-existing temperature adaptations of the recent invader may allow the species to better cope with heat waves.

Ixodes ricinus tick presence is associated with abiotic but not biotic factors.

Species composition and densities of wild ungulate communities in Europe have changed over the last decades. As ungulates play an important role in the life-cycle of the tick species Ixodes ricinus, these changes could affect both the life-cycle of I. ricinus and the transmission of tick-borne pathogens like Borrelia burgdorferi (s.l.) and Anaplasma phagocytophilum. Due to morphological and behavioural differences among the ungulate species, these species might have different effects on the densities of questing I. ricinus, either directly through a bloodmeal or indirectly via the impact of ungulates on rodent numbers via the vegetation. In this study, we aimed to investigate these direct and indirect effects of five different ungulate species, fallow deer (Dama dama), roe deer (Capreolus capreolus), red deer (Cervus elaphus), moose (Alces alces), and wild boar (Sus scrofa), on the presence and abundance of I. ricinus ticks. In the summer of 2019, on 20 1 × 1 km transects in south-central Sweden that differed in ungulate community composition, we collected data on tick presence and abundance (by dragging a cloth), ungulate community composition (using camera traps), vegetation height (using the drop-disc method), temperature above field layer and rodent abundance (by snap-trapping). Using generalized linear mixed models we did not find any associations between vegetation height and tick presence/abundance or ungulate visitation frequencies, or between ungulate visitation frequencies and the presence/abundance of questing I. ricinus. The power of our analyses was, however, low due to very low tick and rodent numbers. We did find a negative association between adult ticks and air temperature, where we were more likely to find adult ticks if temperature in the field layer was lower. We conclude that more elaborate long-term studies are needed to elucidate the investigated associations. Such future studies should differentiate among the potential impacts of different ungulate species instead of treating all ungulate species as one group.

Exploring the influence of host community composition on the outbreak potential of Anaplasma phagocytophilum and Borrelia burgdorferi s.l.

In large parts of the northern hemisphere, multiple deer species coexist, and management actions can strongly influence wild deer communities. Such changes may also indirectly influence other species in the community, such as small mammals and birds, because deer can have strong effects on their habitats and resources. Deer, small mammals and birds play an important role in the dynamics of tick-borne zoonotic diseases. It is, however, relatively underexplored how the abundance and composition of vertebrate communities may affect the outbreak potential, maintenance and circulation of tick-borne pathogens. In this study we focus on the outbreak potential by exploring how the basic reproduction number R0 for different tick-borne pathogens depends on host community composition. We used published data on co-varying roe deer (Capreolus capreolus) and fallow deer (Dama dama) densities following a hunting ban, and different small mammal and bird densities, to investigate how the change in host community influences the R0 of four tick-borne pathogens: one non-zoonotic, namely Anaplasma phagocytophilum ecotype 2, and three zoonotic, namely A. phagocytophilum ecotype 1, Borrelia afzelii and Borrelia garinii. We calculated R0 using a next generation matrix approach, and used elasticities to quantify the contributions to R0 of the different groups of host species. The value of R0 for A. phagocytophilum ecotype 1 was higher with high fallow deer density and low roe deer density, while it was the other way round for A. phagocytophilum ecotype 2. For B. afzelii, R0 was mostly related to the density of small mammals and for B. garinii it was mostly determined by bird density. Our results show that the effect of species composition is substantial in the outbreak potential of tick-borne pathogens. This implies that also management actions that change this composition, can (indirectly and unintentionally) affect the outbreak potential of tick-borne diseases.

Postexposure subunit vaccination against chronic enteric mycobacterial infection in a natural host.

The control of chronic bacterial diseases with high prevalence in areas of endemicity would strongly benefit from availability of postexposure vaccines. The development of these vaccines against mycobacterial infections, such as (para)tuberculosis, is hampered by lack of experience in natural hosts. Paratuberculosis in cattle is both a mycobacterial disease of worldwide importance and a natural host model for mycobacterial infections in general. The present study showed beneficial effects of therapeutic heat shock protein 70 (Hsp70) vaccination in cattle with naturally acquired chronic infection with Mycobacterium avium subsp. paratuberculosis. Vaccination-induced protection was associated with antibody responses, rather than with induction of specific T helper 1 cells. Targeted therapeutic postexposure vaccination complementary to selective use of antibiotics could be an effective approach for control of chronic mycobacterial infections.

Potential corridors and barriers for plague spread in Central Asia.

BACKGROUND: Plague (Yersinia pestis infection) is a vector-borne disease which caused millions of human deaths in the Middle Ages. The hosts of plague are mostly rodents, and the disease is spread by the fleas that feed on them. Currently, the disease still circulates amongst sylvatic rodent populations all over the world, including great gerbil (Rhombomys opimus) populations in Central Asia. Great gerbils are social desert rodents that live in family groups in burrows, which are visible on satellite images. In great gerbil populations an abundance threshold exists, above which plague can spread causing epizootics. The spatial distribution of the host species is thought to influence the plague dynamics, such as the direction of plague spread, however no detailed analysis exists on the possible functional or structural corridors and barriers that are present in this population and landscape. This study aims to fill that gap. METHODS: Three 20 by 20 km areas with known great gerbil burrow distributions were used to analyse the spatial distribution of the burrows. Object-based image analysis was used to map the landscape at several scales, and was linked to the burrow maps. A novel object-based method was developed - the mean neighbour absolute burrow density difference (MNABDD) - to identify the optimal scale and evaluate the efficacy of using landscape objects as opposed to square cells. Multiple regression using raster maps was used to identify the landscape-ecological variables that explain burrow density best. Functional corridors and barriers were mapped using burrow density thresholds. Cumulative resistance of the burrow distribution to potential disease spread was evaluated using cost distance analysis. A 46-year plague surveillance dataset was used to evaluate whether plague spread was radially symmetric. RESULTS: The burrow distribution was found to be non-random and negatively correlated with Greenness, especially in the floodplain areas. Corridors and barriers showed a mostly NWSE alignment, suggesting easier spreading along this axis. This was confirmed by the analysis of the plague data. CONCLUSIONS: Plague spread had a predominantly NWSE direction, which is likely due to the NWSE alignment of corridors and barriers in the burrow distribution and the landscape. This finding may improve predictions of plague in the future and emphasizes the importance of including landscape analysis in wildlife disease studies.

Estimation of the basic reproduction number for Streptococcus equi spp. equi outbreaks by meta-analysis of strangles outbreak reports.

BACKGROUND: Streptococcus equi spp. equi (S. equi), the cause of strangles in horses, is considered a highly contagious pathogen affecting equines and the equine industry worldwide. Fundamental epidemiological characteristics of outbreaks, such as the basic reproduction number (R0 ), are not well described. OBJECTIVES: Estimate R0 for S. equi in equine populations from outbreak data. STUDY DESIGN: Systematic review and meta-analysis of published and unpublished data. METHODS: A literature search for outbreak reports was carried out. Depending on data available in the reports, the early epidemic growth rate or final attack rate (AR) approach was used to estimate the basic reproduction number for that outbreak. Other recorded outbreak characteristics were the type of housing (group vs. individual). An overall estimate for R0 was computed by meta-analysis. RESULTS: Data from eight outbreaks were extracted from peer-reviewed publications. Data from two additional, non-published outbreaks was also included in the meta-analysis. A conservative estimate for R0 was 2.2 (95% confidence interval [CI] 1.9-2.5). A less conservative estimate, including outbreaks with a 100% AR for which a lower limit R0 was estimated, was 2.7 (95% CI 2.1-3.3). MAIN LIMITATIONS: Few papers describing longitudinal incidence data were found so most estimates were based on the outbreaks' final size. Several outbreaks had a 100% attack rate and could therefore only be included as a lower limit estimate in the meta-analysis. The reported result therefore may be an underestimation. CONCLUSIONS: This estimate for R0 for S. equi informs parameters for future mathematical modelling, quantifies desired preventive vaccine coverage and helps evaluate the effect of prevention strategies through future modelling studies.

Mitigation strategies for pandemic influenza A: balancing conflicting policy objectives.

Mitigation of a severe influenza pandemic can be achieved using a range of interventions to reduce transmission. Interventions can reduce the impact of an outbreak and buy time until vaccines are developed, but they may have high social and economic costs. The non-linear effect on the epidemic dynamics means that suitable strategies crucially depend on the precise aim of the intervention. National pandemic influenza plans rarely contain clear statements of policy objectives or prioritization of potentially conflicting aims, such as minimizing mortality (depending on the severity of a pandemic) or peak prevalence or limiting the socio-economic burden of contact-reducing interventions. We use epidemiological models of influenza A to investigate how contact-reducing interventions and availability of antiviral drugs or pre-pandemic vaccines contribute to achieving particular policy objectives. Our analyses show that the ideal strategy depends on the aim of an intervention and that the achievement of one policy objective may preclude success with others, e.g., constraining peak demand for public health resources may lengthen the duration of the epidemic and hence its economic and social impact. Constraining total case numbers can be achieved by a range of strategies, whereas strategies which additionally constrain peak demand for services require a more sophisticated intervention. If, for example, there are multiple objectives which must be achieved prior to the availability of a pandemic vaccine (i.e., a time-limited intervention), our analysis shows that interventions should be implemented several weeks into the epidemic, not at the very start. This observation is shown to be robust across a range of constraints and for uncertainty in estimates of both R(0) and the timing of vaccine availability. These analyses highlight the need for more precise statements of policy objectives and their assumed consequences when planning and implementing strategies to mitigate the impact of an influenza pandemic.

Methicillin-resistant staphylococcus aureus nosocomial infection has a distinct epidemiological position and acts as a marker for overall hospital-acquired infection trends.

An ongoing healthcare debate is whether controlling hospital-acquired infection (HAI) from methicillin-resistant Staphylococcus aureus (MRSA) will result in lowering the global HAI rate, or if MRSA will simply be replaced by another pathogen and there will be no change in overall disease burden. With surges in drug-resistant hospital-acquired pathogens during the COVID-19 pandemic, this remains an important issue. Using a dataset of more than 1 million patients in 51 acute care facilities across the USA, and with the aid of a threshold model that models the nonlinearity in outbreaks of diseases, we show that MRSA is additive to the total burden of HAI, with a distinct 'epidemiological position', and does not simply replace other microbes causing HAI. Critically, as MRSA is reduced it is not replaced by another pathogen(s) but rather lowers the overall HAI burden. The analysis also shows that control of MRSA is a benchmark for how well all non-S. aureus nosocomial infections in the same hospital are prevented. Our results are highly relevant to healthcare epidemiologists and policy makers when assessing the impact of MRSA on hospitalized patients. These findings further stress the major importance of MRSA as a unique cause of nosocomial infections, as well as its pivotal role as a biomarker in demonstrating the measured efficacy (or lack thereof) of an organization's Infection Control program.

The role of inter-regional mobility in forecasting SARS-CoV-2 transmission.

In this paper, we present a method to forecast the spread of SARS-CoV-2 across regions with a focus on the role of mobility. Mobility has previously been shown to play a significant role in the spread of the virus, particularly between regions. Here, we investigate under which epidemiological circumstances incorporating mobility into transmission models yields improvements in the accuracy of forecasting, where we take the situation in The Netherlands during and after the first wave of transmission in 2020 as a case study. We assess the quality of forecasting on the detailed level of municipalities, instead of on a nationwide level. To model transmissions, we use a simple mobility-enhanced SEIR compartmental model with subpopulations corresponding to the Dutch municipalities. We use commuter information to quantify mobility, and develop a method based on maximum likelihood estimation to determine the other relevant parameters. We show that taking inter-regional mobility into account generally leads to an improvement in forecast quality. However, at times when policies are in place that aim to reduce contacts or travel, this improvement is very small. By contrast, the improvement becomes larger when municipalities have a relatively large amount of incoming mobility compared with the number of inhabitants.